Generally speaking, commercial insurance is a form of risk allocation or management involving the equitable transfer of a potential financial loss, from a group of people and/or other entities to an insurance company, in exchange for a fee. Typically, the insurer collects enough in fees (called premiums) from the insured to cover payments for losses covered under the policies (called claims), overhead, and a profit. Each insured property or item, such as a plot of land, a building, company, vehicle, or piece of equipment, is typically referred to as a “risk.” A grouping of risks, e.g., all the properties insured by an insurer or some portion thereof, is called a “portfolio.”
At any particular point in time, each portfolio of risks has an associated set of past claims and potential future claims. The former is a static, known value, while the latter is an unknown variable. More specifically, for a given portfolio in a given time period, e.g., one year, there may be no claims or a large number of claims, depending on circumstances and factors largely outside the insurer's control. However, to set premiums at a reasonable level, it is necessary to predict or estimate future claims, i.e., from the insurer's perspective it is beneficial to set premiums high enough to cover claims and overhead but not so high as would drive away potential customers. This process of mathematically processing data associated with a risk portfolio to predict or estimate future loss is called “risk modeling.” Traditionally, this has involved using actuarial methods where statistics and probability theory are applied to a risk portfolio as a whole (i.e., with the risks grouped together), and taking into consideration data relating to overall past performance of the risk portfolio.
While existing, actuarial-based methods for risk modeling in the insurance industry are generally effective when large amounts of data are available, they have proven less effective in situations with less on-hand data. This is because the data curves generated with such methods, which are used to estimate future losses, are less accurate when less data is present—in estimating a curve to fit discreet data points, the greater the number of data points, the more accurate the curve. Also, since portfolios are considered as a whole, there is no way to effectively assess individual risks using such methods.